The ability to recognize and interpret voiced sounds of another person is one of the most relied upon functions provided by the human auditory system. However, spoken communication typically occurs in adverse acoustic environments including ambient noise, interfering sounds, background chatter and competing voices. Multi-speaker auditory environments are particularly challenging because a group of voices generally have similar average characteristics. Nevertheless, acoustic isolation of a target voice is a hearing task that unimpaired-hearing listeners are able to accomplish effectively. In turn, unimpaired-hearing listeners are able to engage in spoken communication in highly adverse acoustic environments. Hearing-impaired listeners have more difficulty recognizing and interpreting a target voice, even in favorable acoustic environments. The problem is exacerbated by previously available hearing aids, which are based on simply amplifying sound and improving listening comfort.
Previously available hearing aids typically utilize methods that improve sound quality in terms of simply amplifying sound and listening comfort. However, previously available signal processing techniques do not substantially improve speech intelligibility of a target voice beyond that provided by mere amplification of the entire signal. One reason for this is that it is particularly difficult using previously known signal processing techniques to adequately reproduce in real time the acoustic isolation function performed by an unimpaired human auditory system. Another reason is that previously available techniques that improve listening comfort actually degrade speech intelligibility by removing audible information.
The aforementioned problems stemming from inadequate electronic acoustic isolation are also often found in many machine listening applications, such as those utilized by mobile and non-mobile devices. For example, with respect to smartphones and wearable devices, the performance of voice encoders used for telephony and applications utilizing speech recognition typically suffers in acoustic environments that are even slightly adverse.